9 research outputs found
Mean-Field Games for Distributed Caching in Ultra-Dense Small Cell Networks
In this paper, the problem of distributed caching in dense wireless small
cell networks (SCNs) is studied using mean field games (MFGs). In the
considered SCN, small base stations (SBSs) are equipped with data storage units
and cooperate to serve users' requests either from files cached in the storage
or directly from the capacity-limited backhaul. The aim of the SBSs is to
define a caching policy that reduces the load on the capacity-limited backhaul
links. This cache control problem is formulated as a stochastic differential
game (SDG). In this game, each SBS takes into consideration the storage state
of the other SBSs to decide on the fraction of content it should cache. To
solve this problem, the formulated SDG is reduced to an MFG by considering an
ultra-dense network of SBSs in which the existence and uniqueness of the
mean-field equilibrium is shown to be guaranteed. Simulation results show that
this framework allows an efficient use of the available storage space at the
SBSs while properly tracking the files' popularity. The results also show that,
compared to a baseline model in which SBSs are not aware of the instantaneous
system state, the proposed framework increases the number of served files from
the SBSs by more than 69%.Comment: Accepted for publication at American Control Conference 201
Optimal Caching and Routing in Hybrid Networks
Hybrid networks consisting of MANET nodes and cellular infrastructure have
been recently proposed to improve the performance of military networks. Prior
work has demonstrated the benefits of in-network content caching in a wired,
Internet context. We investigate the problem of developing optimal routing and
caching policies in a hybrid network supporting in-network caching with the
goal of minimizing overall content-access delay. Here, needed content may
always be accessed at a back-end server via the cellular infrastructure;
alternatively, content may also be accessed via cache-equipped "cluster" nodes
within the MANET. To access content, MANET nodes must thus decide whether to
route to in-MANET cluster nodes or to back-end servers via the cellular
infrastructure; the in-MANET cluster nodes must additionally decide which
content to cache. We model the cellular path as either i) a
congestion-insensitive fixed-delay path or ii) a congestion-sensitive path
modeled as an M/M/1 queue. We demonstrate that under the assumption of
stationary, independent requests, it is optimal to adopt static caching (i.e.,
to keep a cache's content fixed over time) based on content popularity. We also
show that it is optimal to route to in-MANET caches for content cached there,
but to route requests for remaining content via the cellular infrastructure for
the congestion-insensitive case and to split traffic between the in-MANET
caches and cellular infrastructure for the congestion-sensitive case. We
develop a simple distributed algorithm for the joint routing/caching problem
and demonstrate its efficacy via simulation.Comment: submitted to Milcom 201
Jointly Optimal Routing and Caching for Arbitrary Network Topologies
We study a problem of fundamental importance to ICNs, namely, minimizing
routing costs by jointly optimizing caching and routing decisions over an
arbitrary network topology. We consider both source routing and hop-by-hop
routing settings. The respective offline problems are NP-hard. Nevertheless, we
show that there exist polynomial time approximation algorithms producing
solutions within a constant approximation from the optimal. We also produce
distributed, adaptive algorithms with the same approximation guarantees. We
simulate our adaptive algorithms over a broad array of different topologies.
Our algorithms reduce routing costs by several orders of magnitude compared to
prior art, including algorithms optimizing caching under fixed routing.Comment: This is the extended version of the paper "Jointly Optimal Routing
and Caching for Arbitrary Network Topologies", appearing in the 4th ACM
Conference on Information-Centric Networking (ICN 2017), Berlin, Sep. 26-28,
201
Efficient Traffic Management Algorithms for the Core Network using Device-to-Device Communication and Edge Caching
Exponentially growing number of communicating devices and the need for faster, more reliable and secure communication are becoming major challenges for current mobile communication architecture. More number of connected devices means more bandwidth and a need for higher Quality of Service (QoS) requirements, which bring new challenges in terms of resource and traffic management. Traffic offload to the edge has been introduced to tackle this demand-explosion that let the core network offload some of the contents to the edge to reduce the traffic congestion. Device-to-Device (D2D) communication and edge caching, has been proposed as promising solutions for offloading data. D2D communication refers to the communication infrastructure where the users in proximity communicate with each other directly. D2D communication improves overall spectral efficiency, however, it introduces additional interference in the system. To enable D2D communication, efficient resource allocation must be introduced in order to minimize the interference in the system and this benefits the system in terms of bandwidth efficiency. In the first part of this thesis, low complexity resource allocation algorithm using stable matching is proposed to optimally assign appropriate uplink resources to the devices in order to minimize interference among D2D and cellular users.
Edge caching has recently been introduced as a modification of the caching scheme in the core network, which enables a cellular Base Station (BS) to keep copies of the contents in order to better serve users and enhance Quality of Experience (QoE). However, enabling BSs to cache data on the edge of the network brings new challenges especially on deciding on which and how the contents should be cached. Since users in the same cell may share similar content-needs, we can exploit this temporal-spatial correlation in the favor of caching system which is referred to local content popularity. Content popularity is the most important factor in the caching scheme which helps the BSs to cache appropriate data in order to serve the users more efficiently. In the edge caching scheme, the BS does not know the users request-pattern in advance. To overcome this bottleneck, a content popularity prediction using Markov Decision Process (MDP) is proposed in the second part of this thesis to let the BS know which data should be cached in each time-slot. By using the proposed scheme, core network access request can be significantly reduced and it works better than caching based on historical data in both stable and unstable content popularity
Approximation caching and routing algorithms for massive mobile data delivery
Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations has been proposed in order to decrease the capacity-and hence the cost- of the backhaul links that connect these base stations with the core network. However, deriving the optimal caching policy remains a challenging open problem especially if one considers realistic parameters such as the bandwidth limitation of the base stations. The latter constraint is particularly important for cases when users requests are massive. We consider such a scenario and formulate the joint caching and routing problem aiming to maximize the fraction of content requests served by the deployed small cell base stations. This is an NP-hard problem and hence we cannot obtain an exact optimal solution. Thus, we present a novel approximation framework based on a reduction to a well known variant of the facility location problem. This allows us to exploit the rich literature in facility location problems, in order to establish bounded approximation algorithms for our problem. © 2013 IEEE